Visualizing the Philips Curve

Visualizing the Philips Curve

The Philips Curve was initially discovered as a statistical relationship between unemployment and inflation. The original version used historical US data.

Philips Curve

Our goal here is to visually inspect the Philips curve using recent data, for several countries.

In the process we will learn to: - import dataframes, inspect them, merge them, clean the resulting data - use matplotlib to create graphs - bonus: experiment with other plotting libraries

Importing the Data

We start by loading library dbnomics which contains all the data we want. It is installed already on the nuvolos server.

import dbnomics

The following code imports data for from dbnomics for a few countries.

table_1 = dbnomics.fetch_series(
    [
    "OECD/DP_LIVE/FRA.CPI.TOT.AGRWTH.Q",
    "OECD/DP_LIVE/GBR.CPI.TOT.AGRWTH.Q",
    "OECD/DP_LIVE/USA.CPI.TOT.AGRWTH.Q",
    "OECD/DP_LIVE/DEU.CPI.TOT.AGRWTH.Q"
    ]
)

table_2 = dbnomics.fetch_series([
    "OECD/MEI/DEU.LRUNTTTT.STSA.Q",
    "OECD/MEI/FRA.LRUNTTTT.STSA.Q",
    "OECD/MEI/USA.LRUNTTTT.STSA.Q",
    "OECD/MEI/GBR.LRUNTTTT.STSA.Q"
])

Describe concisely the data that has been imported (periodicity, type of measure, …). You can either check dbnomics website or look at the databases.

The data comes from dbnomics. Provider is OECD. Database is “Data Live dataset” for inflation, and “Main Economic Indicators Publication” for unemployement.

Data is for several countries (Germany, France, USA, Great Britain).

• inflation: Consumer Price Index for all goods and services (total), in annual growth rate, measured every quarter
• unemployment: Labour Force Survey - quarterly rates , workers aged 15 or over

Show the first rows of each database. Make a list of all columns.

table_1.head(2)

	@frequency	provider_code	dataset_code	dataset_name	series_code	series_name	original_period	period	original_value	value	LOCATION	INDICATOR	SUBJECT	MEASURE	FREQUENCY	Country	Indicator	Subject	Measure	Frequency
0	quarterly	OECD	DP_LIVE	OECD Data Live dataset	FRA.CPI.TOT.AGRWTH.Q	France – Inflation (CPI) – Total – Annual grow...	1956-Q1	1956-01-01	1.746324	1.746324	FRA	CPI	TOT	AGRWTH	Q	France	Inflation (CPI)	Total	Annual growth rate (%)	Quarterly
1	quarterly	OECD	DP_LIVE	OECD Data Live dataset	FRA.CPI.TOT.AGRWTH.Q	France – Inflation (CPI) – Total – Annual grow...	1956-Q2	1956-04-01	1.838658	1.838658	FRA	CPI	TOT	AGRWTH	Q	France	Inflation (CPI)	Total	Annual growth rate (%)	Quarterly

table_1.columns

Index(['@frequency', 'provider_code', 'dataset_code', 'dataset_name',
       'series_code', 'series_name', 'original_period', 'period',
       'original_value', 'value', 'LOCATION', 'INDICATOR', 'SUBJECT',
       'MEASURE', 'FREQUENCY', 'Country', 'Indicator', 'Subject', 'Measure',
       'Frequency'],
      dtype='object')

table_2.columns

Index(['@frequency', 'provider_code', 'dataset_code', 'dataset_name',
       'series_code', 'series_name', 'original_period', 'period',
       'original_value', 'value', 'LOCATION', 'SUBJECT', 'MEASURE',
       'FREQUENCY', 'Country', 'Subject', 'Measure', 'Frequency'],
      dtype='object')

Compute standard statistics for all variables

table_1.describe()

	period	value
count	1084	1083.000000
mean	1989-09-30 18:46:29.667896704	3.891054
min	1956-01-01 00:00:00	-1.623360
25%	1972-10-01 00:00:00	1.680341
50%	1989-10-01 00:00:00	2.724924
75%	2006-10-01 00:00:00	5.002851
max	2023-07-01 00:00:00	26.565810
std	NaN	3.591647

table_2.describe()

	period	original_value	value
count	817	817.000000	817.000000
mean	1994-11-08 05:59:33.561811584	6.098632	6.098632
min	1955-01-01 00:00:00	0.373774	0.373774
25%	1979-07-01 00:00:00	4.366667	4.366667
50%	1996-07-01 00:00:00	5.833333	5.833333
75%	2011-01-01 00:00:00	7.996948	7.996948
max	2023-10-01 00:00:00	13.066667	13.066667
std	NaN	2.550835	2.550835

Average inflation over the period for all the countries is 3.89. Average unemployement over the period for all the countries is 6.09.

Compute averages and standard deviations for unemployment and inflation, per country.

# option 1: by using pandas boolean selection 

# we want to extract a subdataframe for each country

ind = table_1['Country'] == 'France' 
table_1_fr = table_1[ ind ]

# what are the unique values taken by the column country ?

# set(table_1['Country']) # pure python
table_1['Country'].unique()

array(['France', 'United Kingdom', 'United States', 'Germany'],
      dtype=object)

table_1_fra = table_1.query("Country=='France'")
table_1_gbr = table_1.query("Country=='United Kingdom'")
table_1_deu = table_1.query("Country=='Germany'")
table_1_usa = table_1.query("Country=='United States'")

d = dict()
for country in ["France", "United Kingdom", "Germany", "United States"]:
    d[country] = table_1.query(f"Country=='{country}'")

# list comprehension
[table_1.query(f"Country=='{country}'") for country in table_1['Country'].unique()];

# dictionary comprehension
d = {country: table_1.query(f"Country=='{country}'") for country in table_1['Country'].unique()}

for k,v in d.items():
    print(f"{k}, mean: {v['value'].mean()}")

France, mean: 4.218004559985239
United Kingdom, mean: 5.003996036162361
United States, mean: 3.678550917822878
Germany, mean: 2.659118566666667

# option 2: by using groupby

table_1.groupby("Country")['value'].agg('mean')

Country
France            4.218005
Germany           2.659119
United Kingdom    5.003996
United States     3.678551
Name: value, dtype: float64

#standard devition
table_1.groupby("Country")['value'].agg('std')

Country
France            3.853190
Germany           1.866131
United Kingdom    4.768593
United States     2.779505
Name: value, dtype: float64

table_1.groupby("Country")['value'].agg(['mean','std'])

	mean	std
Country
France	4.218005	3.853190
Germany	2.659119	1.866131
United Kingdom	5.003996	4.768593
United States	3.678551	2.779505

table_1.groupby("Country")['value'].agg('describe')

	count	mean	std	min	25%	50%	75%	max
Country
France	271.0	4.218005	3.853190	-0.423247	1.660842	2.670692	5.765484	18.565260
Germany	270.0	2.659119	1.866131	-0.922850	1.421377	2.107098	3.441268	8.580543
United Kingdom	271.0	5.003996	4.768593	-0.453172	2.000000	3.244983	6.133533	26.565810
United States	271.0	3.678551	2.779505	-1.623360	1.784109	3.023983	4.523969	14.505600

The following command merges the two databases together. Explain the role of argument on. What happened to the column names?

# we have two dataframes with similar columns

table_1.columns

Index(['@frequency', 'provider_code', 'dataset_code', 'dataset_name',
       'series_code', 'series_name', 'original_period', 'period',
       'original_value', 'value', 'LOCATION', 'INDICATOR', 'SUBJECT',
       'MEASURE', 'FREQUENCY', 'Country', 'Indicator', 'Subject', 'Measure',
       'Frequency'],
      dtype='object')

table_2.columns

Index(['@frequency', 'provider_code', 'dataset_code', 'dataset_name',
       'series_code', 'series_name', 'original_period', 'period',
       'original_value', 'value', 'LOCATION', 'SUBJECT', 'MEASURE',
       'FREQUENCY', 'Country', 'Subject', 'Measure', 'Frequency'],
      dtype='object')

table = table_1.merge(table_2, on=["period", 'Country']) 

table.columns

Index(['@frequency_x', 'provider_code_x', 'dataset_code_x', 'dataset_name_x',
       'series_code_x', 'series_name_x', 'original_period_x', 'period',
       'original_value_x', 'value_x', 'LOCATION_x', 'INDICATOR', 'SUBJECT_x',
       'MEASURE_x', 'FREQUENCY_x', 'Country', 'Indicator', 'Subject_x',
       'Measure_x', 'Frequency_x', '@frequency_y', 'provider_code_y',
       'dataset_code_y', 'dataset_name_y', 'series_code_y', 'series_name_y',
       'original_period_y', 'original_value_y', 'value_y', 'LOCATION_y',
       'SUBJECT_y', 'MEASURE_y', 'FREQUENCY_y', 'Subject_y', 'Measure_y',
       'Frequency_y'],
      dtype='object')

We rename the new names for the sake of clarity and normalize everything with lower cases.

table = table.rename(columns={
    'period': 'date',         # because it sounds more natural
    'Country': 'country',
    'value_x': 'inflation',
    'value_y': 'unemployment'
})

On the merged table, compute at once all the statistics computed before (use groupby and agg).

table.groupby('country')[ ['unemployment', 'inflation'] ].agg('mean')

	unemployment	inflation
country
France	8.680560	1.664349
Germany	4.989272	2.730136
United Kingdom	6.705114	5.404707
United States	5.880812	3.678551

# the resulting dataframe sitll has horrible column names
table.head()

	@frequency_x	provider_code_x	dataset_code_x	dataset_name_x	series_code_x	series_name_x	original_period_x	date	original_value_x	inflation	...	original_period_y	original_value_y	unemployment	LOCATION_y	SUBJECT_y	MEASURE_y	FREQUENCY_y	Subject_y	Measure_y	Frequency_y
0	quarterly	OECD	DP_LIVE	OECD Data Live dataset	FRA.CPI.TOT.AGRWTH.Q	France – Inflation (CPI) – Total – Annual grow...	2003-Q1	2003-01-01	2.366263	2.366263	...	2003-Q1	7.922234	7.922234	FRA	LRUNTTTT	STSA	Q	Labour Force Survey - quarterly rates > Unempl...	Level, rate or national currency, s.a.	Quarterly
1	quarterly	OECD	DP_LIVE	OECD Data Live dataset	FRA.CPI.TOT.AGRWTH.Q	France – Inflation (CPI) – Total – Annual grow...	2003-Q2	2003-04-01	1.912854	1.912854	...	2003-Q2	8.089598	8.089598	FRA	LRUNTTTT	STSA	Q	Labour Force Survey - quarterly rates > Unempl...	Level, rate or national currency, s.a.	Quarterly
2	quarterly	OECD	DP_LIVE	OECD Data Live dataset	FRA.CPI.TOT.AGRWTH.Q	France – Inflation (CPI) – Total – Annual grow...	2003-Q3	2003-07-01	1.93227	1.932270	...	2003-Q3	8.036090	8.036090	FRA	LRUNTTTT	STSA	Q	Labour Force Survey - quarterly rates > Unempl...	Level, rate or national currency, s.a.	Quarterly
3	quarterly	OECD	DP_LIVE	OECD Data Live dataset	FRA.CPI.TOT.AGRWTH.Q	France – Inflation (CPI) – Total – Annual grow...	2003-Q4	2003-10-01	2.184437	2.184437	...	2003-Q4	8.349410	8.349410	FRA	LRUNTTTT	STSA	Q	Labour Force Survey - quarterly rates > Unempl...	Level, rate or national currency, s.a.	Quarterly
4	quarterly	OECD	DP_LIVE	OECD Data Live dataset	FRA.CPI.TOT.AGRWTH.Q	France – Inflation (CPI) – Total – Annual grow...	2004-Q1	2004-01-01	1.800087	1.800087	...	2004-Q1	8.518631	8.518631	FRA	LRUNTTTT	STSA	Q	Labour Force Survey - quarterly rates > Unempl...	Level, rate or national currency, s.a.	Quarterly

5 rows × 36 columns

Before we process further, we should tidy the dataframe by keeping only what we need. - Keep only the columns date, country, inflation and unemployment - Drop all na values - Make a copy of the result

# there are some nas in the dataframe
sum(df['inflation'].isna())

1

table[
    ['inflation', 'unemployment'] # list of columns to select
];
table[['inflation', 'unemployment']];

df = table[['date', 'country', 'inflation', 'unemployment']].dropna()

df = df.copy()
# note: the copy() function is here to avoid keeping references to the original database

What is the maximum available interval for each country? How would you proceed to keep only those dates where all datas are available? In the following we keep the resulting “cylindric” database.

Our DataFrame is now ready for further analysis !

Plotting using matplotlib

Our goal now consists in plotting inflation against unemployment to see whether a pattern emerges. We will first work on France.

from matplotlib import pyplot as plt

Create a database df_fr which contains only the data for France.

df_fr = df.query("country=='France'")

The following command create a line plot for inflation against unemployment. Can you transform it into a scatterplot ?

plt.plot(df_fr['unemployment'], df_fr['inflation']) # missing 'o'

# create a scatter plot
plt.plot(df_fr['unemployment'], df_fr['inflation'], 'o')

Expand the above command to make the plot nicer (label, title, grid, …)

# create a scatter plot
plt.plot(df_fr['unemployment'], df_fr['inflation'], 'o')
plt.title("Philips Curve")
plt.xlabel("Unemployment")
plt.ylabel("Inflation")
plt.grid()

Visualizing the regression

The following piece of code regresses inflation on unemployment.

from statsmodels.formula import api as sm
model = sm.ols(formula='inflation ~ unemployment', data=df_fr)
result = model.fit()
result.summary()

OLS Regression Results

Dep. Variable:	inflation	R-squared:	0.435
Model:	OLS	Adj. R-squared:	0.428
Method:	Least Squares	F-statistic:	62.46
Date:	Wed, 07 Feb 2024	Prob (F-statistic):	1.17e-11
Time:	10:39:59	Log-Likelihood:	-120.37
No. Observations:	83	AIC:	244.7
Df Residuals:	81	BIC:	249.6
Df Model:	1
Covariance Type:	nonrobust

	coef	std err	t	P>|t|	[0.025	0.975]
Intercept	9.7054	1.024	9.479	0.000	7.668	11.743
unemployment	-0.9263	0.117	-7.903	0.000	-1.160	-0.693

Omnibus:	9.014	Durbin-Watson:	0.241
Prob(Omnibus):	0.011	Jarque-Bera (JB):	10.906
Skew:	0.520	Prob(JB):	0.00428
Kurtosis:	4.439	Cond. No.	79.0

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

We can use the resulting model to “predict” inflation from unemployment.

result.predict(df_fr['unemployment'])

0     2.366810
1     2.211777
2     2.261342
3     1.971104
4     1.814349
        ...   
78    3.064908
79    3.055976
80    3.141252
81    2.926298
82    2.888369
Length: 83, dtype: float64

Store the result in df_fr as a new column reg_unemployment

# df_fr.loc['reg_inflation'] = result.predict(df_fr['unemployment'])

# no error message for full index specification
df_fr.loc[:,'reg_inflation'] = result.predict(df_fr['unemployment'])

/tmp/ipykernel_63599/2161117277.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_fr.loc[:,'reg_inflation'] = result.predict(df_fr['unemployment'])

df_fr.head()

	date	country	inflation	unemployment	reg_inflation
0	2003-01-01	France	2.366263	7.922234	2.366810
1	2003-04-01	France	1.912854	8.089598	2.211777
2	2003-07-01	France	1.932270	8.036090	2.261342
3	2003-10-01	France	2.184437	8.349410	1.971104
4	2004-01-01	France	1.800087	8.518631	1.814349

Add the regression line to the scatter plot.

# create a scatter plot
plt.plot(df_fr['unemployment'], df_fr['inflation'], 'o')
plt.plot(df_fr['unemployment'], df_fr['reg_inflation'])

plt.title("Philips Curve")
plt.xlabel("Unemployment")
plt.ylabel("Inflation")
plt.grid()

Now we would like to compare all countries. Can you find a way to represent the data for all of them (all on one graph, using subplots…) ?

df.head()

	date	country	inflation	unemployment
0	2003-01-01	France	2.366263	7.922234
1	2003-04-01	France	1.912854	8.089598
2	2003-07-01	France	1.932270	8.036090
3	2003-10-01	France	2.184437	8.349410
4	2004-01-01	France	1.800087	8.518631

countries_list = df['country'].unique()
countries_list

array(['France', 'United Kingdom', 'United States', 'Germany'],
      dtype=object)

from statsmodels.formula import api as sm

for country in countries_list:
    print(f"country=='{country}'")
    df_country = df.query(f"country=='{country}'")
    model = sm.ols(formula='inflation ~ unemployment', data=df_country)
    result = model.fit()
    
    df_country.loc[:,'reg_inflation'] = result.predict(df_country['unemployment'])
    
    # create a scatter plot
    plt.plot(df_country['unemployment'], df_country['inflation'], '.', label=country, alpha=0.5)
    plt.plot(df_country['unemployment'], df_country['reg_inflation'])
    
    plt.title("Philips Curve")
    plt.xlabel("Unemployment")
    plt.ylabel("Inflation")
    plt.legend()
    plt.grid()

country=='France'
country=='United Kingdom'
country=='United States'
country=='Germany'

/tmp/ipykernel_63599/2982871465.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_country.loc[:,'reg_inflation'] = result.predict(df_country['unemployment'])
/tmp/ipykernel_63599/2982871465.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_country.loc[:,'reg_inflation'] = result.predict(df_country['unemployment'])
/tmp/ipykernel_63599/2982871465.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_country.loc[:,'reg_inflation'] = result.predict(df_country['unemployment'])
/tmp/ipykernel_63599/2982871465.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_country.loc[:,'reg_inflation'] = result.predict(df_country['unemployment'])

# maybe nicer with subplots

plt.subplot(2,2,1)
plt.subplot(2,2,2)
plt.subplot(2,2,3)
plt.subplot(2,2,4)

# i = 0

# for country in countries_list:

#     i = i + 1

# enumeration syntax

for i,country in enumerate(countries_list):
    
    print(f"country=='{country}'")
    df_country = df.query(f"country=='{country}'")
    model = sm.ols(formula='inflation ~ unemployment', data=df_country)
    result = model.fit()
    
    df_country.loc[:,'reg_inflation'] = result.predict(df_country['unemployment'])
    
    # create a scatter plot

    plt.subplot(2,2, i+1)
    plt.plot(df_country['unemployment'], df_country['inflation'], '.', label=country, alpha=0.5)
    plt.plot(df_country['unemployment'], df_country['reg_inflation'])
    
    plt.title("Philips Curve")
    plt.xlabel("Unemployment")
    plt.ylabel("Inflation")
    plt.legend()
    plt.grid()
plt.tight_layout()

country=='France'
country=='United Kingdom'
country=='United States'
country=='Germany'

/tmp/ipykernel_63599/566381394.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_country.loc[:,'reg_inflation'] = result.predict(df_country['unemployment'])
/tmp/ipykernel_63599/566381394.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_country.loc[:,'reg_inflation'] = result.predict(df_country['unemployment'])
/tmp/ipykernel_63599/566381394.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_country.loc[:,'reg_inflation'] = result.predict(df_country['unemployment'])
/tmp/ipykernel_63599/566381394.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_country.loc[:,'reg_inflation'] = result.predict(df_country['unemployment'])

Any comment on these results?

Bonus: Visualizing data using altair

Altair is a visualization library (based on Vega-lite) which offers a different syntax to make plots.

It is well adapted to the exploration phase, as it can operate on a full database (without splitting it like we did for matplotlib). It also provides some data transformation tools like regressions, and ways to add some interactivity.

import altair as alt

chart = alt.Chart(df).mark_point()
chart

The following command makes a basic plot from the dataframe df which contains all the countries. Can you enhance it by providing a title and encoding information to distinguish the various countries (for instance colors)?

chart = alt.Chart(df).mark_point().encode(
    x='unemployment',
    y='inflation',
    # add something here
)
chart

The following graph plots a regression line, but for all countries, it is rather meaningless. Can you restrict the data to France only?

# modify the following code
chart = alt.Chart(df).mark_point().encode(
    x='unemployment',
    y='inflation',
)
chart + chart.transform_regression('unemployment', 'inflation').mark_line()

One way to visualize data consists in adding some interactivity. Add some title and click on the legend

#run first then modify the following code

multi = alt.selection_multi(fields=["country"])

legend = alt.Chart(df).mark_point().encode(
    y=alt.Y('country:N', axis=alt.Axis(orient='right')),
    color=alt.condition(multi, 'country:N', alt.value('lightgray'), legend=None)
).add_selection(multi)

chart_2 = alt.Chart(df).mark_point().encode(
    x='unemployment',
    y='inflation',
    color=alt.condition(multi, 'country:N', alt.value('lightgray')),
    # find a way to separate on the graph data from France and US
)

chart_2 | legend

/home/pablo/.local/opt/micromamba/envs/escp/lib/python3.12/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'selection_multi' is deprecated.  Use 'selection_point'
  warnings.warn(message, AltairDeprecationWarning, stacklevel=1)
/home/pablo/.local/opt/micromamba/envs/escp/lib/python3.12/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'add_selection' is deprecated. Use 'add_params' instead.
  warnings.warn(message, AltairDeprecationWarning, stacklevel=1)

Bonus question: in the following graph you can select an interval in the left panel to select some subsample. Can you add the regression line(s) corresponding to the selected data to the last graph?

brush = alt.selection_interval(encodings=['x'],)

historical_chart_1 = alt.Chart(df).mark_line().encode(
    x='date',
    y='unemployment',
    color='country'
).add_selection(
    brush
)
historical_chart_2 = alt.Chart(df).mark_line().encode(
    x='date',
    y='inflation',
    color='country'
)
chart = alt.Chart(df).mark_point().encode(
    x='unemployment',
    y='inflation',
    # find a way to separate on the graph data from France and US
    color=alt.condition(brush, 'country:N', alt.value('lightgray'))
)
alt.hconcat(historical_chart_1, historical_chart_2, chart,)

/home/pablo/.local/opt/micromamba/envs/escp/lib/python3.12/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'add_selection' is deprecated. Use 'add_params' instead.
  warnings.warn(message, AltairDeprecationWarning, stacklevel=1)

Bonus 2: Plotly Express

Another popular option is the plotly library for nice-looking interactive plots. Combined with dash or shiny, it can be used to build very powerful interactive interfaces.

import plotly.express as px

fig = px.scatter(df, x='unemployment', y='inflation', color='country', title="Philips Curves")
fig

/home/pablo/.local/opt/micromamba/envs/escp/lib/python3.12/site-packages/plotly/express/_core.py:2065: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  sf: grouped.get_group(s if len(s) > 1 else s[0])

Improve the graph above in any way you like